Author: Cathy Yeh

Our last post showed how to obtain the least-squares solution for linear regression and discussed the idea of sampling variability in the best estimates for the coefficients. In this post, we continue the discussion about uncertainty in linear regression — both in the estimates of individual linear regression coefficients and the quality of the overall fit.

Specifically, we’ll discuss how to calculate the 95% confidence intervals and p-values from hypothesis tests that are output by many statistical packages like python’s statsmodels or R. An example with code is provided at the end.

If you have VirtualBox and Vagrant, you can follow a similar procedure on your own computer. The advantage is that you can develop locally, then deploy on an expensive AWS EC2 gpu instance when your scripts are ready.(more…)

Want a quick and easy way to play around with deep learning libraries? Puny GPU got you down? Thanks to Amazon Web Services (AWS) — specifically, AWS Elastic Compute Cloud (EC2) — no data scientist need be left behind.

Jupyter/IPython notebooks are indispensable tools for learning and tinkering. This post shows how to set up a public Jupyter notebook server in EC2 and then access it remotely through your web browser, just as you would if you were using a notebook launched from your own laptop.(more…)

Reinstalling software and configuring settings on a new computer is a pain. After my latest hard drive failure set the stage for yet another round of download-extract-install and configuration file twiddling, it was time to overhaul my approach. "Enough is enough!"

This post walks through

how to back up and automate the installation and configuration process

“SVMs are among the best (and many believe are indeed the best) ‘off-the-shelf’ supervised learning algorithms.”

Professor Ng covers SVMs in his excellent Machine Learning MOOC, a gateway for many into the realm of data science, but leaves out some details, motivating us to put together some notes here to answer the question: